销量约束下基于切片递归神经网络模型的成品油价格推荐算法

连会强; 刘兵; 李朋远; 于华

doi:10.7523/j.ucas.2021.0056

中国科学院大学学报 >

2023 , Vol. 40 >Issue 4: 566 - 576

DOI: https://doi.org/10.7523/j.ucas.2021.0056

电子信息与计算机科学

销量约束下基于切片递归神经网络模型的成品油价格推荐算法

连会强 ,
刘兵 ,
李朋远 ,
于华

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1 中国科学院大学工程科学学院, 北京 100049;
2 中国石油天然气集团公司河北分公司, 石家庄 050000;
3 珠海世纪鼎利科技股份有限公司, 广东珠海 519000

收稿日期: 2021-03-31

修回日期: 2021-08-02

网络出版日期: 2021-08-02

基金资助

国家自然科学基金（71450009）和中国石油天然气集团河北省公司物联网加油站应用项目资助

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A fuel price recommendation model based on the sliced recurrent neural network under sales constraints

LIAN Huiqiang ,
LIU Bing ,
LI Pengyuan ,
YU Hua

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1 School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China;
2 PetroChina Hebei Marketing Company, Shijiazhuang 050000, China;
3 DingLi Corporation Ltd., Zhuhai 519000, Guangdong, China

Received date: 2021-03-31

Revised date: 2021-08-02

Online published: 2021-08-02

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摘要

加油站成品油零售价格的确定是智慧加油站发展的关键。由于成品油价格的变化遵循复杂的非线性规律，尽管以长短期记忆（LSTM）为代表的非线性时序模型提高了传统时序预测方法的精度，但其运行效率难以满足动态变化的油价预测需求。针对这一问题，提出一种基于切片递归神经网络（SRNN）的成品油价格推荐模型，该模型以LSTM模型为递归单元，创新性地通过决策者根据多源数据得到的聚类结果筛选、设置的市场环境因子，对成品油销量施加影响，从而实现在销售约束条件下的成品油价格推荐。基于4年的加油站历史数据对模型预测性能进行了评估。结果表明，使用该模型与LSTM神经网络具有相同的预测精度水平，但比LSTM神经网络的运行速度快72倍。此外，基于SRNN模型的成品油价格推荐算法，加油站在实际销售中得到有效的应用，验证该模型的实用价值。

关键词： 长短期记忆人工神经网络; 价格推荐算法; 智慧加油站; 条件切片循环人工神经网络; 不完整多视角聚类

本文引用格式

连会强 , 刘兵 , 李朋远 , 于华 . 销量约束下基于切片递归神经网络模型的成品油价格推荐算法[J]. 中国科学院大学学报, 2023 , 40(4) : 566 -576 . DOI: 10.7523/j.ucas.2021.0056

Abstract

Determining the retail fuel price for the petrol stations is essential for the development of smart petrol stations. Since the changes in the fuel price follow a complex nonlinear model, the nonlinear time series mode represented by long short-term memory (LSTM) have improved the accuracy of traditional time series forecasting methods, though, its running efficiency is still difficult to meet the dynamic demand of oil price forecasting. To address this issue, in this paper we propose a fuel price recommendation model based on the sliced recurrent neural network (SRNN) with an LSTM model as the recurrent unit under the sales constraints. We further train this model and evaluate its learning parameters, such as learning rate, based on 4 years of historical data. In our evaluations, we utilize real data from petrol stations. Results show that our proposed model achieves the same level of accuracy as that of the LSTM neural network; however, it is 72 times faster than that of the LSTM neural network. Besides, the fuel price recommendation model based on the SRNN is efficiently applied to real petrol stations hence confirmed its practical value.

Key words： long short-term memory network (LSTM); price recommendation; smart gas station; conditional sliced recurrent neural network; incomplete multi-view clustering

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